Editorial Type:
Article
Article Type:
Research Article

Mesoscale Precipitation Fields. Part I: Statistical Analysis and Hydrologic Response

Augusto J. Pereira Fo. Department of Atmospheric Sciences, University of São Paulo, Sao Paulo, Brazil

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Kenneth C. Crawford School of Meteorology, University of Oklahoma, Norman, Oklahoma

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Print Publication:
01 Jan 1999
DOI:
https://doi.org/10.1175/1520-0450(1999)038<0082:MPFPIS>2.0.CO;2
Page(s):
82–101
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Abstract

A statistical objective analysis (SOA) scheme was developed to adjust estimates of rainfall accumulation from the WSR-88D in central Oklahoma using rain gauge measurements from the Oklahoma Mesonetwork. Statistical parameters of these rainfall fields were obtained from a time series of 2-km Constant Altitude Plan Position Indicators at 2 km × 2 km resolution for accumulation time intervals of 15, 30, 60, and 120 min. Results document that the Twin Lakes WSR-88D underestimates rainfall rates by 28% on average. In turn, these errors generate large errors in the streamflow simulations performed for the Dry Creek watershed in north-central Oklahoma. Mean normalized expected error variances of the accumulated rainfall over 1–2-h duration were reduced up to 25% by the SOA scheme. These results are discussed in the context of a hydrometeorological forecast system that uses the analyzed rainfall field to adjust rainfall rates for nowcasting (0–3 h), to improve rainfall forecasts (0–12 h) via its assimilation into mesoscale models, and to verify the accuracy of these rainfall forecasts.

Corresponding author address: Augusto J. Pereira Fo., Department of Atmospheric Sciences, University of São Paulo, Rua do Matão, 1226-Cidade Universitária, São Paulo, SP, Brazil 05508-900.

Abstract

A statistical objective analysis (SOA) scheme was developed to adjust estimates of rainfall accumulation from the WSR-88D in central Oklahoma using rain gauge measurements from the Oklahoma Mesonetwork. Statistical parameters of these rainfall fields were obtained from a time series of 2-km Constant Altitude Plan Position Indicators at 2 km × 2 km resolution for accumulation time intervals of 15, 30, 60, and 120 min. Results document that the Twin Lakes WSR-88D underestimates rainfall rates by 28% on average. In turn, these errors generate large errors in the streamflow simulations performed for the Dry Creek watershed in north-central Oklahoma. Mean normalized expected error variances of the accumulated rainfall over 1–2-h duration were reduced up to 25% by the SOA scheme. These results are discussed in the context of a hydrometeorological forecast system that uses the analyzed rainfall field to adjust rainfall rates for nowcasting (0–3 h), to improve rainfall forecasts (0–12 h) via its assimilation into mesoscale models, and to verify the accuracy of these rainfall forecasts.

Corresponding author address: Augusto J. Pereira Fo., Department of Atmospheric Sciences, University of São Paulo, Rua do Matão, 1226-Cidade Universitária, São Paulo, SP, Brazil 05508-900.

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  • Atlas, D., A. Ryzhkov, and D. Zrnic, 1995: Polarimetric tuned Z–R relations and comparison of radar rainfall methods. Preprints, Third Int. Symp. Hydrological Applications of Weather Radars, São Paulo, Brazil, ABRH/IAHR, 386–395.

  • Austin, P. M., 1987: Relation between measured radar reflectivity and surface rainfall. Mon. Wea. Rev.,115, 1053–1070.

  • Bhargava, M., and M. Danard, 1994: Application of optimum interpolation to the analysis of precipitation in complex terrain. J. Appl. Meteor.,33, 508–518.

  • Brandes, E., 1975: Optimizing rainfall estimates with aid of radar. J. Appl. Meteor.,14, 1339–1345.

  • Brock, F. V., K. C. Crawford, R. L. Elliott, G. W. Cuperus, S. J. Stadler, H. L. Johnson, and M. D. Eilts, 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. and Oceanic Technol.,12, 5–19.

  • Calheiros, R. V., 1993: The area–time integral technique for tropical stratiform rainfall. Preprints, 26th Conf. Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 768–770.

  • Crawford, K. C., 1979: Considerations for the design of a hydrologic data network using multivariate sensors. Water Resour. Res.,15, 1752–1762.

  • Creutin, J. D., and C. Obled, 1982: Objective analysis and mapping techniques for rainfall fields: An objective comparison. Water Resour. Res.,18, 413–431.

  • Crum, T. D., and R. L. Alberty, 1993: The WSR-88D and the WSR-88D operational support facility. Bull. Amer. Meteor. Soc.,74, 1669–1687.

  • Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp.

  • Duan, Q., S. Sorooshian, and V. Gupta, 1992: Effective and efficient global optimization for conceptual rainfall–runoff models. Water Resour. Res.,28, 1015–1031.

  • Duchon, C. E., T. M. Renkevens, and W. L. Crosson, 1995: Estimation of daily area-average rainfall during CaPE experiment in central Florida. J. Appl. Meteor,34, 2704–2714.

  • Farajalla, N. S., and B. E. Vieux, 1994: Capturing the essential spatial variability in distributed hydrological modelling: Hydraulic roughness. Hydrol. Process.,8, 221–236.

  • ——, and ——, 1995: Capturing the essential spatial variability in distributed hydrological modelling: Infiltration parameters. Hydrol. Process.,9, 55–68.

  • Gandin, L. S., 1963: Objective Analysis of Meteorological Fields. English translation by R. Harding, Israel Program for Scientific Translation, 242 pp.

  • Groisman, P. Y., and D. R. Legates, 1994: The accuracy of the United States precipitation data. Bull. Amer. Meteor. Soc.,75, 215–227.

  • Hoos, A. B., A. D. Koussis, and G. O. Beale, 1989: A channel dynamic model for real-time flood forecasting. Water Resour. Res.,25, 691–705.

  • Huff, F. A., and W. L. Shipp, 1969: Spatial correlations of storm, monthly and seasonal precipitation. J. Appl. Meteor.,8, 542–550.

  • Kelly, D. S., 1994: A comparison of high resolution precipitation accumulation estimates from the WSR-88D precipitation algorithm with rain gage data. Preprints, First WSR-88D User’s Conf., Norman, OK, WSR-88D OSF and NEXRAD Joint System Program Office, 185–192.

  • Kitchen, M., and P. M. Jackson, 1993: Weather radar performance at long range—Simulated and observed. J. Appl. Meteor.,32, 975–985.

  • Klazura, G. E., and D. A. Imy, 1993: A description of the initial set of analysis products available from the NEXRAD WSR-88D system. Bull. Amer. Meteor. Soc.,74, 1293–1311.

  • Koch S., M. Desjardins, and P. Kocin, 1983: An interactive Barnes objective map analysis scheme for use with satellite and conventional data. J. Climate Appl. Meteor.,22, 1487–1503.

  • Krajewski, W. F., 1987: Cokringing radar-rainfall and rain gage data. J. Geophys. Res.,92, 9571–9580.

  • Legates, D. R., and T. L. DeLiberty, 1993: Measurement biases in the United Sates raingage network, Water Resour. Bull.,29, 855–861.

  • Massambani, O., and A. J. Pereira Fo., 1991: The diurnal evolution of the ground return intensities and its use as a radar calibration procedure. Hydrological Applications of Weather Radars, Clukie I. D. and C. G. Collier, Eds., Ellis Horwood, 143–150.

  • Messaoud, M., and Y. B. Pointin, 1990: Small time and space measurement of the mean rainfall rate made by a gauge network and by a dual-polarization radar. J. Appl. Meteor.,29, 830–841.

  • Michaud, J., and S. Sorooshian, 1994: Comparison of simple versus complex distributed runoff models on a midsized semiarid watershed. Water Resour. Res.,30, 593–605.

  • Morrissey, M. L., J. A. Maliekal, J. S. Greene, and J. Wang, 1995: The uncertainty of simple spatial averages using rain gauge networks. Water Resour. Res.,31, 2011–2017.

  • Pereira Fo., A. J., K. C. Crawford, and D. J. Stensrud, 1999: Mesoscale precipitation fields. Part II: Hydrometeorologic modeling. J. Appl. Meteor.,38, 102–125.

  • Ryzhkov, A. V., and D. S. Zrnic, 1994: Precipitation observed in Oklahoma mesoscale convective systems with a polarimetric radar. J. Appl. Meteor.,33, 455–464.

  • Seed, A. W., J. Nicol, G. L. Austin, C. D. Stow, and S. G. Bradley, 1995: A physical basis for parameter selection for Z–R relationships. Preprints, Third Int. Symp. on Hydrological Applications of Weather Radars, São Paulo, Brazil, ABRH/IAHR, 100–108.

  • Seo, D.-J., and E. R. Johnson, 1995: The WSR-88D precipitation processing subsystem: An overview and performance evaluation. Preprints, Third Int. Symp. on Hydrological Applications of Weather Radars, São Paulo, Brazil, ABRH/IAHR, 222–231.

  • Smith, J. A., D. J. Seo, M. L. Baeck, and M. D. Hudlow, 1996: An intercomparison study of NEXRAD precipitation estimates. Water Resour. Res.,32, 2035–2045.

  • Thiebaux, H. J., 1976: Anisotropic correlation function for objective analysis. Mon. Wea. Rev.,104, 994–1002.

  • ——, 1977: Extending estimation accuracy with anisotropic interpolation. Mon. Wea. Rev.,105, 691–699.

  • U.S. Soil Conservation Service, 1970: Soil Survey of Lincoln County, 57 pp.

  • ——, 1972: National Engineering Handbook; Hydrology. Water Resources, 589 pp.

  • ——, 1982: TR-20 Computer Program for Project Formulation Hydrology. Northeast NTC and Hydrology Unit Soil Conservation Service, 168 pp.

  • Weber, R. O., and P. Talkner, 1993: Some remarks on spatial correlation function models. Mon. Wea. Rev.,121, 2611–2617.

  • Wilson, J. W., and E. A. Brandes, 1979: Radar measurement of rainfall—A summary. Bull. Amer. Meteor. Soc.,60, 1048–1058.

  • Wyss, J., E. R. Williams, and R. L. Bras, 1990: Hydrologic modeling of New England river basins using radar rainfall data. J. Geophys. Res.,95, 2143–2152.

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